Josh Dillon, Last Revised January 2022
This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "222" csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_" auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 17 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_ Found 17 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0
def jd_to_summary_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'
def jd_to_auto_metrics_url(jd):
return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'
this_antenna = None
jds = []
# parse information about antennas and nodes
for csv in csvs:
df = pd.read_csv(csv)
for n in range(len(df)):
# Add this day to the antenna
row = df.loc[n]
if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
else:
antnum = int(row['Ant'])
if antnum != int(antenna):
continue
if np.issubdtype(type(row['Node']), np.integer):
row['Node'] = str(row['Node'])
if type(row['Node']) == str and row['Node'].isnumeric():
row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
if this_antenna is None:
this_antenna = Antenna(row['Ant'], row['Node'])
jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
jds.append(jd)
this_antenna.add_day(jd, row)
break
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]
df = pd.DataFrame(to_show)
# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
df[col] = bar_cols[col]
z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
df[col] = z_score_cols[col]
ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
df[col] = ant_metrics_cols[col]
redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]
for col in redcal_cols:
df[col] = redcal_cols[col]
# style dataframe
table = df.style.hide_index()\
.applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
.background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
.background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
.applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
.applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
.bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
.format({col: '{:,.4f}'.format for col in z_score_cols}) \
.format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
.format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
.set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])])
This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))
| JDs | A Priori Status | Auto Metrics Flags | Dead Fraction in Ant Metrics (Jee) | Dead Fraction in Ant Metrics (Jnn) | Crossed Fraction in Ant Metrics | Flag Fraction Before Redcal | Flagged By Redcal chi^2 Fraction | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | Average Dead Ant Metric (Jee) | Average Dead Ant Metric (Jnn) | Average Crossed Ant Metric | Median chi^2 Per Antenna (Jee) | Median chi^2 Per Antenna (Jnn) |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 2459994 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.071388 | -0.371247 | -0.168082 | -0.046858 | -0.902734 | -0.370531 | 2.942181 | -0.953471 | 0.6026 | 0.6266 | 0.3738 | nan | nan |
| 2459991 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.492410 | -0.255641 | -0.077934 | 0.186498 | -0.639906 | -0.619087 | 2.797483 | -1.216475 | 0.6009 | 0.6177 | 0.3817 | nan | nan |
| 2459990 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.335695 | 0.465386 | -0.128869 | -0.974367 | -0.382915 | 36.439857 | 2.026995 | -0.298393 | 0.5992 | 0.5714 | 0.3793 | nan | nan |
| 2459989 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.021935 | -0.286811 | 0.087719 | -0.621919 | -0.480429 | 18.403297 | 1.842386 | -0.882684 | 0.5953 | 0.6036 | 0.3772 | nan | nan |
| 2459988 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.122360 | -0.073182 | -0.137175 | 0.306295 | -0.384522 | -0.657662 | 2.351488 | -1.269526 | 0.5996 | 0.6252 | 0.3731 | nan | nan |
| 2459987 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.335609 | -0.341086 | -0.122166 | 0.016240 | -0.641568 | -0.929282 | 1.644398 | -1.611868 | 0.6045 | 0.6260 | 0.3712 | nan | nan |
| 2459986 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.163110 | -0.189295 | -0.156637 | 0.202234 | -0.890075 | -0.691712 | 0.718970 | -1.047430 | 0.6255 | 0.6506 | 0.3376 | nan | nan |
| 2459985 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | -0.156012 | -0.255635 | -0.168729 | -0.012419 | -0.848962 | -1.085096 | 5.006342 | -1.218745 | 0.6096 | 0.6299 | 0.3777 | nan | nan |
| 2459984 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | -0.368998 | -0.689880 | -0.000553 | 0.027332 | -1.061359 | -1.178449 | 0.117963 | -1.398931 | 0.6280 | 0.6481 | 0.3582 | nan | nan |
| 2459983 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.153976 | -0.189269 | -0.176394 | 0.241103 | -0.696682 | -0.562581 | 1.707239 | -0.857146 | 0.6299 | 0.6605 | 0.3247 | nan | nan |
| 2459982 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.596627 | -0.919615 | -0.685554 | -0.341307 | -0.344799 | -1.057247 | -0.970134 | -1.343898 | 0.6857 | 0.6904 | 0.2861 | nan | nan |
| 2459981 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.429502 | -0.077953 | -0.141478 | 0.403027 | -0.813761 | -0.870732 | 2.418557 | -1.279564 | 0.6065 | 0.6308 | 0.3754 | nan | nan |
| 2459980 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.180608 | -0.222266 | -0.376290 | -0.179147 | -0.393324 | 0.517563 | -0.424249 | -1.380356 | 0.6496 | 0.6643 | 0.3053 | nan | nan |
| 2459979 | RF_ok | 100.00% | 0.00% | 0.00% | 0.00% | - | - | 0.572213 | -0.332203 | -0.361116 | -0.575190 | -0.521614 | 15.998415 | 1.717768 | -1.090410 | 0.5951 | 0.6161 | 0.3758 | nan | nan |
| 2459978 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.381975 | -0.233959 | -0.222269 | -0.224974 | -0.547812 | 3.261101 | 3.160486 | -1.239247 | 0.5956 | 0.6208 | 0.3842 | nan | nan |
| 2459977 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.309760 | -0.191490 | -0.317790 | -0.057133 | -0.004015 | -1.184705 | 2.682394 | -1.671503 | 0.5636 | 0.5889 | 0.3449 | nan | nan |
| 2459976 | RF_ok | 0.00% | 0.00% | 0.00% | 0.00% | - | - | 0.519280 | -0.080663 | -0.345065 | -0.138692 | -0.379738 | 1.563726 | 2.852930 | -1.195304 | 0.6043 | 0.6287 | 0.3733 | nan | nan |
auto_metrics notebooks.¶htmls_to_display = []
for am_html in auto_metric_htmls:
html_to_display = ''
# read html into a list of lines
with open(am_html) as f:
lines = f.readlines()
# find section with this antenna's metric plots and add to html_to_display
jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
try:
section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
except ValueError:
continue
html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
for line in lines[section_start_line + 1:]:
html_to_display += line
if '<hr' in line:
htmls_to_display.append(html_to_display)
break
These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.
for i, html_to_display in enumerate(htmls_to_display):
if i == 100:
break
display(HTML(html_to_display))
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 2.942181 | 0.071388 | -0.371247 | -0.168082 | -0.046858 | -0.902734 | -0.370531 | 2.942181 | -0.953471 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 2.797483 | 0.492410 | -0.255641 | -0.077934 | 0.186498 | -0.639906 | -0.619087 | 2.797483 | -1.216475 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | nn Temporal Variability | 36.439857 | 0.465386 | 0.335695 | -0.974367 | -0.128869 | 36.439857 | -0.382915 | -0.298393 | 2.026995 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | nn Temporal Variability | 18.403297 | -0.286811 | 0.021935 | -0.621919 | 0.087719 | 18.403297 | -0.480429 | -0.882684 | 1.842386 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 2.351488 | -0.073182 | 0.122360 | 0.306295 | -0.137175 | -0.657662 | -0.384522 | -1.269526 | 2.351488 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 1.644398 | -0.335609 | -0.341086 | -0.122166 | 0.016240 | -0.641568 | -0.929282 | 1.644398 | -1.611868 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 0.718970 | -0.189295 | 0.163110 | 0.202234 | -0.156637 | -0.691712 | -0.890075 | -1.047430 | 0.718970 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 5.006342 | -0.255635 | -0.156012 | -0.012419 | -0.168729 | -1.085096 | -0.848962 | -1.218745 | 5.006342 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 0.117963 | -0.368998 | -0.689880 | -0.000553 | 0.027332 | -1.061359 | -1.178449 | 0.117963 | -1.398931 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 1.707239 | 0.153976 | -0.189269 | -0.176394 | 0.241103 | -0.696682 | -0.562581 | 1.707239 | -0.857146 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Shape | 0.596627 | 0.596627 | -0.919615 | -0.685554 | -0.341307 | -0.344799 | -1.057247 | -0.970134 | -1.343898 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 2.418557 | -0.077953 | 0.429502 | 0.403027 | -0.141478 | -0.870732 | -0.813761 | -1.279564 | 2.418557 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | nn Temporal Variability | 0.517563 | -0.222266 | 0.180608 | -0.179147 | -0.376290 | 0.517563 | -0.393324 | -1.380356 | -0.424249 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | nn Temporal Variability | 15.998415 | 0.572213 | -0.332203 | -0.361116 | -0.575190 | -0.521614 | 15.998415 | 1.717768 | -1.090410 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | nn Temporal Variability | 3.261101 | -0.233959 | 0.381975 | -0.224974 | -0.222269 | 3.261101 | -0.547812 | -1.239247 | 3.160486 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | ee Shape Modified Z-Score | nn Shape Modified Z-Score | ee Power Modified Z-Score | nn Power Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Discontinuties Modified Z-Score | nn Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 2.682394 | 0.309760 | -0.191490 | -0.317790 | -0.057133 | -0.004015 | -1.184705 | 2.682394 | -1.671503 |
| Ant | Node | A Priori Status | Worst Metric | Worst Modified Z-Score | nn Shape Modified Z-Score | ee Shape Modified Z-Score | nn Power Modified Z-Score | ee Power Modified Z-Score | nn Temporal Variability Modified Z-Score | ee Temporal Variability Modified Z-Score | nn Temporal Discontinuties Modified Z-Score | ee Temporal Discontinuties Modified Z-Score |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 222 | N18 | RF_ok | ee Temporal Discontinuties | 2.852930 | -0.080663 | 0.519280 | -0.138692 | -0.345065 | 1.563726 | -0.379738 | -1.195304 | 2.852930 |